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Ultrasound Image Classification of Thyroid Nodules Using Machine Learning Techniques.

Vijay Vyas Vadhiraj1,2, Andrew Simpkin3, James O'Connell1,2

  • 1School of Medicine, College of Medicine Nursing and Health Sciences, National University of Ireland Galway, H91 TK33 Galway, Ireland.

Medicina (Kaunas, Lithuania)
|June 2, 2021
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Summary

This study developed a computer system using multiple-instance learning (MIL) to classify thyroid nodules as benign or malignant. The system, integrating Thyroid Imaging Reporting and Data System (TI-RADS) features, showed promising results for thyroid cancer detection.

Keywords:
AIANNCADSVMTI-RADSartificial intelligencebenignbig datacancercomputer aided diagnosticsdigital healthmalignant

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence in Medicine
  • Oncology

Background:

  • Thyroid nodules are common and can be malignant or benign.
  • Accurate differentiation between benign and malignant thyroid nodules is crucial for patient management.
  • Current diagnostic methods can be improved with advanced computational tools.

Purpose of the Study:

  • To evaluate the effectiveness of a computer-aided diagnosis system using multiple-instance learning (MIL) for thyroid nodule classification.
  • To assess if integrating Thyroid Imaging Reporting and Data System (TI-RADS) features improves diagnostic decision-making.
  • To compare the performance of Support Vector Machine (SVM) and Artificial Neural Network (ANN) algorithms in classifying thyroid nodules.

Main Methods:

  • Development of a computer-aided diagnosis system employing MIL for benign-malignant classification of thyroid nodules.
  • Image pre-processing using median filter and binarization, followed by feature extraction with Grey Level Co-occurrence Matrix (GLCM).
  • Comparison of SVM and ANN classification algorithms using accuracy, sensitivity, and specificity on a dataset of 99 thyroid nodule cases.

Main Results:

  • The Support Vector Machine (SVM) algorithm achieved a high accuracy of 96%, outperforming the Artificial Neural Network (ANN) which achieved 75%.
  • SVM demonstrated superior performance across all metrics, including accuracy, sensitivity, and specificity.
  • A graphic user interface (GUI) was developed to aid radiologists in visualizing image features for decision support.

Conclusions:

  • Multiple-instance learning (MIL) shows significant potential for improving thyroid cancer detection accuracy.
  • The developed SVM-based classification model demonstrates high efficacy in differentiating benign from malignant thyroid nodules.
  • Further validation with external datasets is recommended for clinical implementation of the proposed classification model.